Typical surveillance systems include a plurality of sensors that collects data and/or monitor for security threats based on predetermined conditions. For example, the plurality of sensors may include video cameras. Typical video surveillance systems may include a plurality of video cameras that monitor a large geographic area. For example, the large geographic area may be a warehouse. Each of the plurality of video cameras may collect metadata corresponding to a monitored area.
A typical video surveillance system typically involve a plurality of video feeds being streamed to one or more surveillance monitors. A human operator may be required to simultaneously monitor the plurality of video feeds from the plurality of video cameras, and thus, some security threats may not be detected. Therefore, video surveillance systems may include automated detection systems that monitor areas based on normal motion models already known to the automated video surveillance system. The video surveillance systems may use these normal motion models as a reference when analyzing one or more of the video feeds.
Previous automated detection systems may detect “abnormal behavior” in real-time from surveillance footage and the normal motion models. (e.g. U.S. patent application Ser. No. 11/676,127). The automatic detection system may alert the human operator of a potential security threat when abnormal behaviors are detected in the real-time surveillance video feed. The operator may analyze the potential security threat and choose whether to actuate an alarm. Additionally, the automatic detection system may actuate an alarm without notifying the operator. Furthermore, the automatic detection system may store metadata corresponding to the potential security threat for updating of the predetermined conditions and/or future analysis of the potential security threat.
For example, U.S. Pat. No. 7,088,846 discloses a video surveillance system that uses rule-based reasoning and multiple-hypothesis scoring to detect predetermined object behavior based on object movement. The system determines an alert condition based on the movement patterns of an object. The alert condition may be defined by an occurrence of a combination of particular events. For example only, the particular events may include an appearance of a person, a movement of the person towards a door, or the person swiping an object at a card reader. The system may determine whether the particular events have occurred and may determine a time stamp for each of the particular events. The system may then determine whether an alert condition has occurred based on predefined rules.
However, the system requires that an entire rule set is to be configured by the operator. Furthermore, the system requires that the particular events are to be based on a particular sequence of the particular events. Thus, these requirements may make it difficult to completely define a model of abnormal behavior for a moderate-sized to large-sized rule set.
Furthermore, there is a lack of surveillance systems that are able to predict abnormal behavior before it occurs. Such a system may prevent various security breeches and save lives, rather than report such breeches or accidents once they have occurred.
The background description provided herein is for the purpose of generally presenting the context of the disclosure. Work of the presently named inventors, to the extent the work is described in this background section, as well as aspects of the description that may not otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present disclosure.